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UPLC-Q/TOF MS standardized Chinese formula Xin-Ke-Shu for the treatment of atherosclerosis in a rabbit model.

ABSTRACT

Xin-Ke-Shu (XKS), a patent traditional Chinese medicine (TCM) preparation, has been commonly used for the treatment of coronary heart disease in China. In order to understand its mechanism of action, a metabonomic approach based on ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF MS) was utilized to profile the plasma metabolic fingerprints of atherosclerosis (AS) rabbits with and without XKS treatment. The metabolic profile of model group clearly separated from normal, and that of XKS group was closer to the control group. Metabolites with significant changes during atherosclerosis were characterized as potential biomarkers related to the development of atherosclerosis by using orthogonal partial least-squares-discriminate analysis (OPLS-DA). Twenty potential biomarkers, including L-acetylcarnitine (1), propionylcarnitine (2), unknown (3), phytosphingosine (4), glycoursodeoxycholic acid (5), LPC(14:0) (6), sphinganine (7), LPC(20:5) (8), LPC(16:1) (9), LPC(18:2) (10), LPC(18:3) (11), LPC(22:5) (12), LPC(16:0) (13), LPC(18:1) (14), LPC(22:4) (15), LPC(17:0) (16), LPC(20:2) (17), elaidic carnitine (18), LPC(18:0) (19) and LPC(20:1) (20), were identified by their accurate mass and MSE spectra. The derivations of those biomarkers can be regulated by administration of XKS, which suggested that the intervention effect of XKS against AS may involve in regulating the lipid perturbation including fatty acid [beta]-oxidation pathway, sphingolipid metabolism, glycerophospholipid metabolism and bile acid biosynthesis. This study indicated that the UPLC-Q/TOF MS-based metabonomics not only gave a systematic view of the pathomechanism of AS, but also provided a powerful tool to study the efficacy and mechanism of complex TCM prescriptions.

Keywords:

Atherosclerosis

Xin-Ke-Shu

Plasma metabonomics

Pharmacological action

Lipid pathways

Introduction

Atherosclerosis (AS), a major contributor to cardiovascular diseases (CVDs), is a pathophysiological process with a multifactorial nature (Sidney et al., 2013). The underlying pathological process is a thickening of the arterial wall due to the formation of the atherosclerotic plaques. There are a number of genetic, metabolic, and environmental factors involved in the formation and evolution of the atherosclerotic plaque (Ambrose and Srikanth, 2010; Naghavi et al., 2003), which is characterized by the injury to vessel wall caused by lipid accumulation, chronic inflammation, cell death, and thrombosis (Braunwald, 1997). Recently, great advances in drug treatment for AS have been achieved, but their adverse effects were still perplexed the pharmaceutical scientists (Fitzgerald et al., 2006; Zimmermann et al., 2007). Traditional Chinese medicines for the prevention and treatment of atherosclerosis are gaining more attentions all over the world, due to their specific theory and long historical clinical practice (Guo et al., 2011; Wing-Shing et al., 2012; Zhang et al., 2013).

Xin-Ke-Shu (XKS) is a traditional Chinese medicine for the treatment of coronary heart and cerebrovascular disease with few side effects (Chinese Pharmacopoeia Commission, 2010). It is a really complex matrix, which comprises five herbs, including the roots of Salvia miltiorrhiza Bge. (Dan-Shen), the roots of Pueraria lobata (Willd.) Ohwi. (Ge-Gen), the roots of Panax notoginseng (Burk.) F. H. Chen. (San-Qi), the fruit of Crataegus pitmatifida Bge. (Shan-Zha) and the roots of Aucldandia lappa Decne (Mu-Xiang). The chemical constituents in XKS preparation were qualitatively and quantitatively investigated by an optimized LC-LTQ-Orbitrap method (Peng et al., 2011a). Our recent study indicated that pretreatment of XKS could protect plasma metabolic perturbations in rats with MI major via lipid pathways, amino acids metabolism and purine metabolism (Liu et al., 2014). Additionally, XKS could rescue coronary endothelial injury in atherosclerotic MI rabbits via regulating the expressions of endothelial nitric oxide synthase and vascular cell adhesion molecule (Xu et al., 2012). Thus, investigation into the whole biochemical variation is required for deeply understanding the molecular mechanism of XKS against AS.

Metabonomics, as an important platform of systems biology, has been defined as "the quantitative measurement of the dynamic multi-parametric metabolic response of living organism to pathophysiological stimulation or genetic modification" (Nicholson et al., 1999). Metabonomic studies hold promise for the discovery of metabolic pathways linked to disease process and pharmacological action of drugs (Nicholson et al. 2004; Weckwerth and Morgenthal, 2005). Emerging metabonomic evidences have also proved that the lipid species were significant related to AS (Zhang et al., 2009; Jove et al., 2013; Teul et al., 2009; Martin et al., 2009; Kleemann et al., 2007; Peng et al., 2011b).

In the present study, the metabolic profiles and potential biomarkers in a rabbit model of atherosclerosis with or without XKS treatment were investigated using UPLC-Q/TOF MS based metabonomics, which may facilitate understanding the pathological changes of AS and the protection mechanism of XKS. To date, this is the first report of TCM protecting from AS rabbit using a metabonomic approach based on UPLC-Q/TOF MS.

Materials and methods

Reagents and materials

Standardized XKS tablets comprised with Dan-Shen, Ge-Gen, Shan-Zha, San-Qi, and Mu-Xiang at the ratio of 15:15:15:1:1 were supplied by a GMP pharmaceutical company, Wo Hua Pharmaceutical Co, CHN (batch No. 090629), and its quality control was performed using an LC-LTQ-Orbitrap method (Peng et al., 2012). Five herbs, including the roots of Dan-Shen, the roots of Ge-Gen, the roots of San-Qi, the fruit of Shan-Zha and the roots of Mu-Xiang were authenticated as the roots of Salviae miltiorrhizae Bge., the roots of Pueraria lobata (Willd.) Ohwi., the roots of Panax notoginseng (Burk.) F. H. Chen., the fruit of Crataegus pinnatifide Bge. and the roots of Aucklandia lappa Decne by Prof. Bengang Zhang from Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences. The voucher specimens have been deposited in the Herbarium of the Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing, China. Atorvastatin was purchased fromjialin Pharmaceutical Co CHN. Cholesterol was from Tian Qi Chemical Engineering Co, CHN. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL) and low-density lipoprotein cholesterol (LDL) were purchased from Biosino Bio-technology & Science INC (Beijing, China). HPLC-grade acetonitrile was purchased from J.T. Baker (Phillipsburg, NJ, SA). Ultrapure water (18.2 ML2) was prepared with a Milli-Q water purification system (Millipore, France). All other chemicals were of analytical grade.

Animal handling and sample collection

Twenty-four male Japanese white rabbits (weighting 2.2 [+ or -] 0.2 kg, aged 3 weeks) were obtained from the Laboratory Animal Institute of the Chinese Academy of Medical Science. Animals were housed individually in cages with food and water freely available in Specific Pathogen Free Laboratory. The protocol of the study was approved by the Ethics Committee of the Institute of Medicinal Plant Development, CAMS and PUMC (Beijing, China). After one week of adaptation, the rabbits were randomly separated into 4 groups (n = 6) according to the body weights and the level of plasma total cholesterol. Control group received a diet of standard rabbit chow (SC). Model group was treated with a high cholesterol diet (HCD) (standard rabbit chow supplemented with 3% w/w cholesterol, 0.7% w/w sodium cholate, and 0.1% w/w thiamazole). Positive group received HCD and atorvastatin at a dose of 4 mg/kg/d. The XKS treated group were treated with a high cholesterol diet (HCD) and XKS tablets at a dose of 0.34g/kg/d. The duration of the treatment was 12 weeks. Diet and water were available ad libitum during the experimental period.

Blood samples were collected into Na-heparin tubes and plasma was obtained after centrifugation (3000 x g, 4[degrees]C for 10 min) (Legend Micro 17R, Thermo, USA). The plasma samples were frozen at -80[degrees]C before analysis. At the end of experiment, all rabbits were euthanatized and subjected to autopsy. Their tissues of heart and aorta were removed and fixed in 10% formalin for histopathology examination.

Clinical chemical analysis and histopathology

TC, TG, HDL and LDL were measured with a standard spectrophotometric method using a HITACHI 7060 automatic analyzer.

To investigate the histopathological changes of rabbits in each group, the aortas were isolated from aortic arch to the end piece of thoracic aorta, stained with hematoxylin and eosin (HE), and examined by light microscopy. Image analysis was carried out using Image-Pro Plus (Version 5.0). The atherosclerotic plaques ratio was calculated as percent lesion area per total area of the aorta. Left circumflex coronary artery (2 cm long) with the adjacent myocardium tissues was carefully cut. The paraffin-embed slices were stained with hematoxylin and eosin (HE) and scanned by using NanoZoomer digital pathology image analysis system (Hamamatus, Olympus, JAP). The coronary stenosis ratio was calculated as percent lesion area per total area of the aorta.

Sample preparation

Plasma (200 [micro]l) was added into acetonitrile (600 [micro]l), vortex-mixing for 30 s, and centrifuged at 6000 x g for 10 min to precipitate the proteins. 750 [micro]l of protein free supernatant was collected and dried with nitrogen at 37[degrees]C. The dried residue was reconstituted in 100 [micro]l of acetonitrile-water (1:99, v/v), after centrifugation for 15 min at 13,000 x g, an aliquot of 2 [micro]l was injected for UPLC/MS analysis.

Data acquisition

Chromatographic separation was performed on an Acquity UPLC HSS T3 column (2.1 mm x 100 mm, 1.8 [micro]m, Waters Corp., Milford, USA) using a Waters ACQUITY UPLC system, equipped with a binary solvent delivery system. The column was maintained at 40[degrees]C and eluted at a flowing rate of 0.45 ml/min, using a mobile phase of (A) 0.1% (by volume) formic acid in water and (B) acetonitrile. The gradient program was optimized as follows: 0-0.1 min, 1% B; 0.1-5 min, 1% B to 50% B; 5-9 min, 50% B to 100%B; 9-11 min, washing with 100% B, and 11-13 min, equilibration with 1 % B. The eluent from the column was directed to the mass spectrometer without split.

A Waters SYNAPY G2 HDMS (Waters Corp., Manchester, UK) was used to carry out the mass spectrometry with an electrospray ionization source (ESI) operating in positive ion mode. The capillary voltages, sample cone voltage and extraction cone voltage were set at 3.0 kV, 40 V and 4.0 V, respectively. Used drying gas nitrogen, the desolvation gas rate was set to 8001/h at 450[degrees]C, the cone gas rate at 40 l/h, and the source temperature at 120[degrees]C. The scan time and inter scan delay were set to 0.15 and 0.02 s, respectively. Leucine-enkephalin was used as the lockmass in all analyses (m/z 556.2771) at a concentration of 0.5 [micro]g/mL with a flow rate of 5 [micro]l/min. Data was collected in centroid mode from m/z 100 to m/z 1500. The lock spray frequency was set at 5 s and the lock mass data were averaged over 10 scans for correction.

Multivariate analysis

The raw MS spectra were first analyzed using MarkerLynx Applications Manager version 4.1 (Waters Corp., Manchester, UK), which allowed deconvolution, alignment and data reduction to give a list of mass and retention time pairs with corresponding peak area for all the detected peaks from each file in the data set. The main parameters in MarkerLynx were set as follows: retention time range, 0-11 min; mass range, 100-1200 Da; XIC window, 0.02 min; automatically calculate peak width and peak-peak baseline noise; use the raw data during the deconvolution procedure; marker intensity threshold (count), 1000; mass tolerance, 0.02 Da; retention time windows, 0.2 min; noise elimination level, 6; retain the isotopic peaks.

The processed data list was then exported and processed by the principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) in the software package Simca-P software (v12.0, Umetric, Umea, Sweden). All the tested groups were discriminated in the PCA model. In the OPLS-DA model, samples from two groups were classified, and the results were visualized in the form of score plot to show the group clusters and S-plot to show variables contributing to the classification. To validate the model against overfitting, a typical 7-round cross-validation was carried out with 1/7 of the samples being excluded from the model in each round. This procedure was repeated in an iterative manner until each sample had been excluded once and the [R.sup.2] and [Q.sup.2] values were calculated from the results in Simca-P package.

Statistical analysis

All values were expressed as mean [+ or -] S.D. The significance of differences between the means of the treated and un-treated groups has been compared by a two-tailed Student's t-test using the Statistical Package for Social Science program (SPSS 16.0, Chicago, IL, USA). The significance threshold was set at p < 0.05 for this test.

Results and discussion

Clinical chemical analysis

The plasma biochemical parameters of experimental animals were summarized in Table 1. The concentrations of TC, TG, HDL and LDL in model rabbits were apparently higher than those in control rabbits. Atorvastatin treatment for 12 weeks significantly reduced TC (p < 0.001), HDL (p < 0.001) and LDL (p < 0.01) levels compared with model group. XKS treatment significantly decreased the HDL level compared to the model group (p < 0.01), and showed improving tendency on TC and LDL with no significance. No significant changes were observed in the levels of TG in all drug-treated groups (p > 0.5). These results indicated that XKS might have different therapeutic mechanism compared to atorvastatin.

Histopathology

Typical macroscopic atherosclerotic plaques on the initial surface of aortas were clearly observed in all groups except the control group after 12-week feeding (Fig. 1A). Treatment with XKS and atorvastatin caused a notable decrease in atherosclerotic plaques area ratio compared to the model group (p < 0.5). The effect was similar between positive group and XKS group (p > 0.5). Disease severity and effect of XKS treatment were further verified by coronary stenosis by HE staining (Fig. 1B). Some intramyocardial small arterioles showed significant atherosclerotic changes in model group. Atorvastatin and XKS treatment significantly inhibited the coronary stenosis compared with model group (p<0.01). The histopathological results demonstrated that treatment with XKS and atorvastatin showed similar anti-atherosclerosis effect on the histopathological changes induced by high-fat diet.

Method development

Plasma sample is a complex matrix containing a wide variety of compounds with diverse chemical structures, representing the average metabolic status of the organism. Untargeted metabonomics is a tool to analyze the global system for changes in endogenous metabolites. So the preparation of samples was critical to obtain a comprehensive and abundance metabolome. Here we employed a minimal sample preparation steps to avoid the loss of endogenous metabolites, including deproteinization with methanol, centrifugation and dilution before analysis.

The UPLC system provided a rapid, sensitive and convenient analytical method for the analysis of rabbit plasma. However, the complexity of plasma samples could cause metabolite losses, and matrix effects (ionization suppression) on co-eluting metabolites, which are major problems for LC-MS-based analysis. So we initially attempted to optimize a suitable UPLC method to simultaneously determine the endogenous metabolites as much as possible, accompanying well resolved chromatographic separation. During method development, two different columns, different mobile phases and detecting ion modes were tested. The selection of UPLC columns with high separation efficiency is a prerequisite. Here, two chromatographic columns, BEH (ethylene bridged hybrid) C18 column (2.1 mm x 100 mm, 1.7 [micro]m, Waters) and HSS (high strength silica) T3 column (2.1 mm x 100 mm, 1.8 [micro]m, Waters), were utilized to investigate for the comprehensive metabolome. The BEH Cl 8 column is the universal column choice for UPLC separations. While HSS T3 column with 100% silica particle, is used to retain and separate smaller, more water-soluble polar organic compounds than the BEH Cl 8 column (Zhao et al., 2013). The result showed that HSS T3 column could gain a more extensive retention and a better chromatographic separation for analysts.

Mobile phases including acetonitrile-water and methanol-water with modifiers such as acetic acid, formic acid, and different gradient elution modes were all investigated. The results showed that the mobile phase consisted of water (0.1% formic acid) and acetonitrile (0.1% formic acid) gave the best separation and peak shape.

The plasma samples were measured both in the positive and negative ion modes. We observed that high noise and matrix effect in the negative ion mode with a high baseline, which led to the neglect of some metabolites of low abundance and the concomitance of multiple adduction ions. Therefore, metabolic profiling of plasma samples was acquired in the positive ion mode. Fig. 2A presents the typical positive base peak intensity (BPI) chromatogram of plasma samples from all the experimental groups.

Method validation

To ensure the stability and repeatability of the development method, we prepared a quality control (QC) sample by pooling the same volume (10 [micro]l) from each plasma sample according the reported method (Xiang et al., 2012). The QC sample was operated every 6 plasma samples to evaluate stability during sequence analysis. Ten ions were extracted from the Base Peak Intensity chromatography and selected for method validation. The relative standard deviations (RSD) of retention times, m/z and peak areas of the selected ions were less than 0.5345%, 0.0011% and 13.2547%, respectively. The repeatability of the method was evaluated using six replicates by analyzing QC sample. The RSD of retention times, m/z and peak areas of the selected ions were less than 0.4531%, 0.0045% and 13.3238%, respectively (Table S1). The results demonstrated that the developed method was an available approach for this metabonomic analysis.

Multivariate analysis of UPLC-Q/TOF MS data

Within metabonomics, PCA and OPLS-DA approaches are frequently used to distinguish the differences between experimental groups and screen the variations contributing to the corresponding classification. By firstly using of PCA (Fig. 2B), the metabolic profile of rabbit in the model group deviated from the control. suggesting that significant biochemical changes were induced by AS. Meanwhile, a clear separation among control, AS, positive and XKS groups was observed. The metabolic profile of rabbits in XKS treated group fairly differed from the AS group and closed to the control, indicating the deviations induced by AS were significantly improved after treatment of XKS. The parameters for the classification were [R.sup.2]X=0.609 and [Q.sup.2] = 0.495, which are good to fit and predict, respectively. The system stability was monitored by the QC sample, which was also performed using the PCA analysis (Zhou et al., 2012). It is found that the QC samples cluster together tightly in the score plot of PCA, which indicated that the system stability was accommodative for this metabonomic study (Fig. 2B).

As a consequence, the OPLS-DA method was employed to bring out the special variation between the MI and control groups. Clear separation of the control and model rabbits could be observed, indicating that there was significantly difference in plasma metabolite profiles from the two groups (Fig. 3A). In this work, a well-fitting two-component OPLS-DA model ([R.sup.2]Y = 0.998, [Q.sup.2]Y = 0.984) was constructed to identify and reveal the differential metabolites in response to different groups. S-plot is a tool for visualizing covariance and correlation between the metabolites and the modeled class, those ions far from the origin contributing to the clustering significantly. As shown in Fig. 3B, S-plot based on plasma metabolic profiles indicated control group and model group indicated 19 ions contributed to the clustering with retention time and m/z pairs of 0.64_204.1238, 1.05_218.1395, 2.96_566.4273, 5.80_318.3008, 6.15_450.6310, 6.65_468.3085, 6.67_302.3058, 6.82_542.3241, 6.92_494.3249, 7.10.520.3405, 7.37.518.3219, 7.45_570.3554, 7.55.496.3409, 7.80.522.6751, 7.92.572.3712, 8.01.510.3556, 8.04.548.3711, 8.19.426.3579, 8.48.546.3531 and 8.67.550.3868. They contributed significantly to differentiate the clustering of AS group from that of normal control, could be considered as potential biomarkers responsible for derivations of metabolic profile induced by AS. In addition, the variable importance for projection (VIP) values of these biomarkers was all above 1.

Identification of potential biomarkers associated with XKS treatment

Identification of 20 marker ions was then carried out by the TOF-MS accurate mass measurement and the acquisited [MS.sup.E] spectra. They were identified as L-acetylcarnitine (1), propionylcarnitine (2), unknown (3), phytosphingosine (4), glycoursodeoxycholic acid (5), LPC(14:0) (6), sphinganine (7), LPC(20:5) (8), LPC(16:1) (9), LPC(18:2) (10), LPC(18:3) (11), LPC(22:5) (12), LPC(16:0) (13), LPC( 18:1) (14), LPC(22:4) (15), LPC(17:0) (16), LPC(20:2) (17), elaidic carnitine (18), LPC(18:0) (19) and LPC(20:1) (20), which played significant roles in the formation of AS and contributed to the pharmacological action of XKS (Table 2).

Here, a potential biomarker with m/z 218.1395 at 1.05 min is taken as an example to illustrate the identification process. Using MarkerLynx software, the potential calculated masses, mass accuracy, DBE (total number of rings and double bonds in a molecule), i-FIT value (the likelihood that the isotopic pattern of the elemental composition matches a cluster of peaks in the spectrum), and elemental compositions associated with the measured mass of the candidate metabolites were generated and studied. Using a mass tolerance of 10 ppm, three possible candidates were produced with the elemental composition analysis software implemented in the MarkerLynx. Three candidates representing with calculated masses/mDa/ppm/DBE/i-FIT/elemental compositions of the three candidates were 218.1392/0.3/1.4/15.1/[C.sub.10][H.sub.20]N[O.sub.4], 218.1382/1.3/6.0/3.5/17.2/[C.sub.9][H.sub.17][N.sub.5]Na, and 218.1406/-1.1/- 5.0/5.5/19.1/[C.sub.11][H.sub.16][N.sub.5], respectively. At last, the first candidate ([C.sub.10][H.sub.20]N[O.sub.4], [[M + H].sup.+]) with the high mass accuracy obtained (0.3 mDa or 1.4 ppm), fulfillment of DBE (1.5), and low i-FIT value (15.1) was selected as the final result. We also searched METLIN database (http://metlin.scripps.edu), to identify the biomarkers by using accurate mass and [MS.sup.E] information. The ion of m/z 218.1395 was searched by comparing the extract mass with those enrolled in the METLIN database. The result showed that only one compound propionylcarnitine matching with the accurate mass (mass difference lower than 5 ppm). The fragmentation pathway was also matched with the report of database METLIN (Fig. SI). So the ion of m/z 218.1395 was tentatively identified as propionylcarnitine (2).

The varied tendencies of the identified pathological biomarkers related to AS were shown in Fig. 4. The concentrations of 17 metabolites (3-17, 19 and 20) were significantly increased and 3 (1, 2 and 18) decreased in AS group compared with normal control. XKS pretreatment corrected the variations of pathogenic biomarkers (1-7, 9-12, 13-15, 17, 18 and 20), while atorvastatin did not show any effects on some of the metabolites (2, 6 and 8).

Perturbed metabolic network in response to AS rabbits associated with XKS

Increased dietary cholesterol intake is associated with atherosclerosis. The rabbit fed with high-cholesterol diet has been widely used as an atherosclerosis model, of which the atherosclerotic lesions are characterized by an increase in leukocyte immigration, endothelial penetration, and the formation of macrophage derived foam cells in the intima of the large conduit arteries (especially the aorticarch and thoracicaorta), which are similar to human fatty streaks at the early stage of the lesions (Yanni et al., 1961).

The disturbance of lipid metabolism acted a pivotal pathogenetic role in the initiation and progression of atherosclerosis, which has been demonstrated by numerous studies. Besides the remarkable disorders of t-CHO, TG, HDL and LDL obtained in traditional biochemical analysis, the perturbation of glycerophospholipid metabolism, sphingolipid metabolism and fatty acid oxidation, as well as bile acid biosynthesis was also observed in this study, which was in good agreement with other reports (Fig. 5) (Zhang et al., 2009). Lipid-based metabolites play important roles in many biochemistry reactions and are related to many biological

functions. Pretreatment of XKS could effectively inhibit the perturbation of lipid pathways.

Glycerophospholipid metabolism

Lysophosphatidylcholines (LPCs) are formed by hydrolysis of phosphatidylcholines (PC), which play key roles in the progress of atherosclerosis and inflammatory diseases (Matsumoto et al., 2007). Phosphorylcholine was found to be positively associated with atherosclerotic lesions (Peng et al. 2011b). Increased levels of phosphorylcholine can lead to high concentrations of LPCs, which can trigger inflammation and the autoimmune response in atherosclerosis (Frostegard, 2010). In our study, LPCs (6, 8-17, 19 and 20) were increased in atherosclerosis rabbit, which matched with other reports that LPCs were enhanced in symptomatic carotid atherosclerotic plaques in human (Lavi et al., 2007; Aiyar et al., 2007; Mannheim et al., 2008). XKS treatment showed favorable inhibition of these LPCs, indicated that the depression of XKS on glycerophospholipid metabolism might contribute to its cardioprotective efficacy.

Sphingolipid metabolism

Sphingolipids, a large class of lipids with structural and signaling functions, play important roles in the development of atherosclerosis (Levade et al., 2001). It is known that sphingomyelinases (SMases) hydrolyse sphingomyelin releases ceramide and results in the accumulation of phytosphingosine (4) and sphingasine (7) (Pavoine and Pecker, 2009). It was also reported that the sphingomyelin content of atherosclerotic lesions is higher than that of normal arterial tissue (Zilversmit et al., 1961). The increase levels of these sphingolipids may decrease the reverse cholesterol transport pathway, which might increase the risk of atherosclerosis (Rye et al., 1996; Worgall et al., 2004). The increased levels of phytosphingosine (4) and sphingasine (7) in the plasma samples of AS rabbits suggested that the expression of SMases was up-regulated and the metabolism of sphingolipid was promoted under AS condition. After XKS treatment, the increased levels of 4 and 7 were down-regulated, indicating XKS had regulation effect on sphingolipid metabolism.

Fatty acid [beta]-oxidation pathway

Carnitine cycle is the first step for fatty acid oxidation, in which the fatty acyl CoA enters the mitochondria as fatty acyl carnitines via carnitine transport (Stanley et al., 2005). Under AS condition, the disorder of glycolysis might promote the process of fatty acid oxidation to supply the required energy, in which fatty acyl carnitines are consumed to facilitate the long-chain fatty acid into the mitochondria. Kleemann et al. reported that high cholesterol intake could up-regulate the fatty acid oxidation-related gene expressions in ApoE*3 Leiden transgenic mice, which provided the indirect evidence for their promotion of fatty acids oxidation (Kleemann et al., 2007). It was also reported by A. M. Karz that inhibition of fatty acid metabolism resulted in the accumulation of toxic intermediates such as long-chain acylcarnitine derivatives (Katz and Messineo, 1981). In this study, L-acetylcarnitine (1), propionyl-L-carnitine (2) and elaidic carnitine (18) were decreased in AS rabbit, suggesting that the fatty acid [beta]-oxidation pathway was promoted under AS condition. Their reduction could be inhibited by treatment of XKS, suggesting that the protective efficacy of XKS might ascribe to the inhibition of fatty acid oxidant.

Bile acid biosynthesis

Glycoursodeoxycholic acid (5) is a secondary bile acid produced by the action of enzymes existing in the colonic microbial flora. Bile acids are physiological detergents that facilitate the excretion, absorption, and transport of fats and sterols in the intestine and liver (Marschall et al., 1994). The increased plasma concentration of glycoursodeoxycholic acid (5) in atherosclerosis rabbits was observed, and pretreatment of XKS could effectively inhibit its up-regulation, suggesting XKS could ameliorate the disturbed bile biosynthesis and cholesterol metabolism.

In the present work, a total of twenty altered metabolites related to AS were detected in plasma by LC-MS. These metabolites belong to the categories of glycerophospholipids, sphingolipids, steroids and fatty acids, which were involved into glycerophospholipid metabolism, sphingolipid metabolism, fatty acid [beta]-oxidation pathway and bile acid biosynthesis. These lipid pathways make contribution to AS states involving inflammation, endothelial dysfunction, as well as alterations of energy metabolism in the AS progression. However, NMR-based metabonomics done in our previous study (Peng et al., 2011b) did not detected potential biomarkers related to sphingolipid metabolism, fatty acid [beta]-oxidation pathway and bile acid biosynthesis. Although NMR identified one altered metabolite (phosphorylcholine) involved into glycerophospholipid metabolism, LC-MS identified a series of lysophosphatidylcholines including LPCs (6, 8-17, 19 and 20). The results indicated that the established metabonomic approach based on LC-MS could provide more comprehensive metabolome related to lipid disturbance for atherosclerosis progression.

Conclusion

In this study, an UPLC-Q/TOF MS-based metabonomics was carried out to characterize the global plasma metabolic profile associated with atherosclerosis in rabbits fed with high-cholesterol diet. Twenty metabolites involving into the alteration of energy metabolism, inflammation and endothelial dysfunction, were identified as potential biomarkers related to AS. Among them, lipid pathways played vital roles in the formation of AS. With the presented metabonomic method, we systematically delineated the therapeutic effect of XKS against AS. Treatment of XKS could effectively inhibit the metabolic alternations induced by AS. The findings suggested that atherosclerosis led to perturbation of LPCs, bile acids and sphingolipids, which might be the pharmacological basis of XKS against AS. This research also demonstrated that UPLC-Q/TOF MS-based metabonomic approach was a powerful tool to explore the underlying pathophysiologic mechanism of complex diseases and provide the experimental evaluation of TCMs.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.Org/10.1016/j.phymed. 2014.05.009.

Conflict of interest

The authors declare no conflict of interests.

ARTICLE INFO

Article history:

Received 22 January 2014

Received in revised form 4 April 2014

Accepted 11 May 2014

Acknowledgements

This work has been financially supported by National S & T Major Special Project on Major New Drug Innovation (No. 2013ZX09508104), National Natural Science Foundation of China (No. 81073021) and Program for Innovative Research Team in IMPLAD (No. IT1305)

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Yue-Tao Liu (a), Jing-Bo Peng (a), Hong-Mei Jia (a), Da-Yong Cai (a), Hong-Wu Zhang (a), Chang-Yuan Yu (b), **, Zhong-Mei Zou (a),*

(a) Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, PR China

(b) College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China

* Corresponding author. Tel.: +86 10 57833290: fax: +86 10 57833290.

** Corresponding author. Tel.: +86 10 64448589: fax: +86 10 64448589.

E-mail addresses: yucy@mail.buct.edu.cn (C.-Y. Yu), zmzou@implad.ac.cn (Z.-M. Zou).

http://dx.doi.org/10.1016/j.phymed.2014.05.009

Table 1
Plasma biochemical parameters in control and treatment groups.

Parameters           T-CHO                    TG
(mmol/l)

Control       0.43 [+ or -] 0.15 **   0.52 [+ or -] 0.11 *
Model        28.87 [+ or -] 5.76      1.07 [+ or -] 0.81
Positive      9.98 [+ or -] 4.44 **   0.69 [+ or -] 0.18
XKS          23.86 [+ or -] 11.86     0.82 [+ or -] 0.32

Parameters            LDL                     HDL
(mmol/l)

Control       0.09 [+ or -] 0.03 **   0.36 [+ or -] 0.14 **
Model        15.11 [+ or -] 8.20      3.17 [+ or -] 0.45
Positive      7.62 [+ or -] 3.46 *    2.46 [+ or -] 0.44 **
XKS          10.46 [+ or -] 5.61      2.62 [+ or -] 0.54 *

Student t-test was conducted for statistic analysis.

* p<0.01 compared with model group.
** p< 0.001 compared with model group.

Table 2
Potential biomarkers related to AS detected by UPLC-Q/TOF MS.

No.   Compound                    RT(min)   m/z        Adduct ion

1     L-Acetylcarnitine           0.64      204.1238   [[M+H].sup.+]
2     Propionylcarnitine          1.05      218.1395   [[M+H].sup.+]
3     Unknown                     2.96      566.4273   [[M+H].sup.+]
4     Phytosphingosine            5.80      318.2999   [[M+H].sup.+]
5     Glycoursodeoxycholic acid   6.15      450.6310   [[M+H].sup.+]
6     LPC(14:0)                   6.65      468.3085   [[M+H].sup.+]
7     Sphinganine                 6.67      302.3058   [[M+H].sup.+]
8     LPC(20:5)                   6.82      542.3241   [[M+H].sup.+]
9     LPC( 16:1)                  6.92      494.3249   [[M+H].sup.+]
10    LPC(18:2)                   7.10      520.3405   [[M+H].sup.+]
11    LPC(18:3)                   7.37      518.3219   [[M+H].sup.+]
12    LPC(22:5)                   7.45      570.3554   [[M+H].sup.+]
13    LPC(16:0)                   7.55      496.3409   [[M+H].sup.+]
14    LPC( 18:1)                  7.80      522.6751   [[M+H].sup.+]
15    LPC(22:4)                   7.92      572.3712   [[M+H].sup.+]
16    LPC(17:0)                   8.01      510.3556   [[M+H].sup.+]
17    LPC(20:2)                   8.04      548.3711   [[M+H].sup.+]
18    Elaidic carnitine           8.19      426.3579   [[M+H].sup.+]
19    LPC(18:0)                   8.48      546.3531   [[M+Na].sup.+]
20    LPC(20:1)                   8.67      550.3868   [[M+H].sup.+]

No.   Formula                            VIP

1     [C.sub.9][H.sub.17]N[O.sub.4]      3.62
2     [C.sub.10][H.sub.19]N[O.sub.4]     1.41
3     [C.sub.29][H.sub.60]N[O.sub.7]P    2.10
4     [C.sub.18][H.sub.39]N[O.sub.3]     6.63
5     [C.sub.26][H.sub.43]N[O.sub.5]     3.90
6     [C.sub.22][H.sub.46]N[O.sub.7]P    1.82
7     [C.sub.18][H.sub.39]N[O.sub.2]     6.04
8     [C.sub.28][H.sub.48]N[O.sub.7]P    1.11
9     [C.sub.24][H.sub.48]N[O.sub.7]P    8.07
10    [C.sub.26][H.sub.50]N[O.sub.7]P   21.02
11    [C.sub.26][H.sub.48]N[O.sub.7]P    4.04
12    [C.sub.30][H.sub.52]N[O.sub.7]P    4.14
13    [C.sub.24][H.sub.50]N[O.sub.7]P   23.72
14    [C.sub.26][H.sub.52]N[O.sub.7]P    1.92
15    [C.sub.30][H.sub.54]N[O.sub.7]P    5.05
16    [C.sub.25][H.sub.52]N[O.sub.7]P    1.88
17    [C.sub.28][H.sub.54]N[O.sub.7]P    5.28
18    [C.sub.25][H.sub.47]N[O.sub.4]     2.70
19    [C.sub.26][H.sub.54]N[O.sub.7]P    4.96
20    [C.sub.28][H.sub.56]N[O.sub.7]P    2.86

No.   Pathway

1     Fatty acid [beta]-oxidation pathway
2     Fatty acid [beta]-oxidation pathway
3     Glycerophospholipid metabolism
4     Sphingolipid metabolism
5     Bile acid biosynthesis
6     Glycerophospholipid metabolism
7     Sphingolipid metabolism
8     Glycerophospholipid metabolism
9     Glycerophospholipid metabolism
10    Glycerophospholipid metabolism
11    Glycerophospholipid metabolism
12    Glycerophospholipid metabolism
13    Glycerophospholipid metabolism
14    Glycerophospholipid metabolism
15    Glycerophospholipid metabolism
16    Glycerophospholipid metabolism
17    Glycerophospholipid metabolism
18    Fatty acid [beta]-oxidation pathway
19    Glycerophospholipid metabolism
20    Glycerophospholipid metabolism
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Title Annotation:Cardiovascular System
Author:Liu, Yue-Tao; Peng, Jing-Bo; Jia, Hong-Mei; Cai, Da-Yong; Zhang, Hong-Wu; Yu, Chang-Yuan; Zou, Zhong
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
Geographic Code:9CHIN
Date:Sep 25, 2014
Words:7221
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